This work can be extended in a number of ways. One potential direction is to use different ways of creating model ensembles, for example, by using Dropout [1]} or pseudo-ensembles [2]}. It is interesting to investigate how the proposed algorithm can be combined with other tricks from the PINN literature, for example, adaptive balancing of the loss terms [3]}. Another direction is to find alternative ways of the solution interval expansion (e.g., update sets \(\mathcal {I}_\text{PDE}^{\prime }\) , \(\mathcal {I}_\text{B}^{\prime }\)  more frequently), which may increase the convergence speed.
It would also be useful to improve the PINN architecture such that the knowledge of a found (local) solution in one area could be used in finding the solution in another area. Using neural networks with the right inductive bias (see, e.g., [4]}, [5]}) might provide a solution for that.
